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  license: bigscience-bloom-rail-1.0
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  datasets:
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  - c4
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- - CarperAI/pile-v2-small-filtered
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  language:
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  - en
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  library_name: transformers
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  tokenizer - gpt-j
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  ```
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- Trained on 4,194,304 samples from the [c4](https://hf.co/datasets/c4) dataset, at a length of 128 tokens each, that comes out to 536,870,912 (0.53B) tokens seen during training. A batch size of 16 with 128 gradient accumulation steps was used, making the effective batch size 2048. A cosine learning rate schedule was used starting at 1e-3.
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- Another 20,480 samples from [CarperAI/pile-v2-small-filtered](https://hf.co/CarperAI/pile-v2-small-filtered) were used for finetuning, again at 128 tokens each (for a total of 2.6M more tokens) at a batch size of 16 with 256 gradient accumulation steps, with a learning rate of 1e-4 with a linearly decreasing schedule. (That's 5 entire steps for y'all counting at home, it took ~4:30 lol)
 
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  license: bigscience-bloom-rail-1.0
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  datasets:
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  - c4
 
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  language:
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  - en
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  library_name: transformers
 
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  tokenizer - gpt-j
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  ```
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+ Trained on 4,194,304 samples from the [c4](https://hf.co/datasets/c4) dataset, at a length of 128 tokens each, that comes out to 536,870,912 (0.53B) tokens seen during training. A batch size of 16 with 128 gradient accumulation steps was used, making the effective batch size 2048. A cosine learning rate schedule was used starting at 1e-3.